Loop-closure detection or place recognition is also an important module in SLAM back-end to reduce open-loop errors. Conventional works are based on bag-of-words (BoW) to store and use the visual features from the hand-crafted detectors.
However, this problem is complicated by the changes of illumination, weather, viewpoints and moving objects in real-world scenarios. To solve this, previous researchers such as [1]} proposed to use the ConvNet features instead, that are from a pre-trained model on a generic large-scale image processing dataset. These methods are more robust against the variance of viewpoints and conditions due to the high-level representations extracted by deep neural networks.
Other representative works [2]}, [3]}, [4]} are built on deep auto-encoder structure to extract a compact representation, that compresses scene in an unsupervised manner. Deep learning based loop closing contributes more robust and effective visual features, and achieves state-of-the-art performance in place recognition, which is suitable to be integrated in SLAM systems.
